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If we need to bind multiple dataframes by rows, we may first wish to check if this can be done successfully. The {compare_df_cols} function from {janitor} 📦 provides a summary of data types to check this! 🍎🍏 sfirke.github.io/janitor/ref… #rstats #DataScience

In real use cases, this routine will actually be used to access a variable - specified by the user - from a data frame. Note that now `data$x` won’t work because we don’t know a priori what `x` is going to be.

While working in a piped workflow, if you need to extract a single column, the syntax to do so can be a bit cumbersome. The {pull} function from {dplyr} 📦 provides a more readable syntax to this end! 💄 dplyr.tidyverse.org/referenc… #rstats #DataScience

Visualizing data along an Archimedean spiral can efficiently reveal periodic patterns in time series data. The {spiral_*} function family from {spiralize} 📦 draws such spiral plots flexibly! 🌀 jokergoo.github.io/spiralize… #rstats #DataScience

Serialization changes objects to a byte stream that can be saved to a binary file, while deserializing does the reverse. The {q*} function family from {qs} 📦 provides a performant way to serialize or deserialize any R object! ⚡️ rdrr.io/cran/qs/man/ #rstats #DataScience

Sometimes you may wish to include some patterns or images in plots for enhanced storytelling. The {image*} or {pattern*} function family from {patternplot} 📦 provides a way to do so! ⚜️ rdrr.io/cran/patternplot/man… #rstats #DataScience

Python dictionary is an unordered data type with key-value pairs that allows accessing values, not by indexing, but via unique keys. The {py_dict} function from {reticulate} 📦 gives you access to this data type in R! 📖 rstudio.github.io/reticulate… #rstats #DataScience